To achieve satisfactory performance from automatic medical image analysis
algorithms such as registration or segmentation, medical imaging data with
the desired modality/contrast and high isotropic resolution are preferred, yet
they are not always available. We addressed this problem in this thesis using
1) image modality synthesis and 2) resolution enhancement.
The first contribution of this thesis is computed tomography (CT)-tomagnetic
resonance imaging (MRI) image synthesis method, which was developed
to provide MRI when CT is the only modality that is acquired. The
main challenges are that CT has poor contrast as well as high noise in soft
tissues and that the CT-to-MR mapping is highly nonlinear. To overcome these
challenges, we developed a convolutional neural network (CNN) which is a
modified U-net. With this deep network for synthesis, we developed the first
segmentation method that provides detailed grey matter anatomical labels on
CT neuroimages using synthetic MRI.
The second contribution is a method for resolution enhancement for a
common type of acquisition in clinical and research practice, one in which
there is high resolution (HR) in the in-plane directions and low resolution (LR)
in the through-plane direction. The challenge of improving the through-plane resolution for such acquisitions is that the state-of-art convolutional neural
network (CNN)-based super-resolution methods are sometimes not applicable
due to lack of external LR/HR paired training data. To address this challenge,
we developed a self super-resolution algorithm called SMORE and its iterative
version called iSMORE, which are CNN-based yet do not require LR/HR
paired training data other than the subject image itself. SMORE/iSMORE
create training data from the HR in-plane slices of the subject image itself, then
train and apply CNNs to through-plane slices to improve spatial resolution
and remove aliasing. In this thesis, we perform SMORE/iSMORE on multiple
simulated and real datasets to demonstrate their accuracy and generalizability.
Also, SMORE as a preprocessing step is shown to improve segmentation
accuracy.
In summary, CT-to-MR synthesis, SMORE, and iSMORE were demonstrated
in this thesis to be effective preprocessing algorithms for visual quality
and other automatic medical image analysis such as registration or segmentation
